Fast and Provable ADMM for Learning with Generative Priors

NeurIPS 2019 Fabian Latorre GómezArmin EftekhariVolkan Cevher

In this work, we propose a (linearized) Alternating Direction Method-of-Multipliers (ADMM) algorithm for minimizing a convex function subject to a nonconvex constraint. We focus on the special case where such constraint arises from the specification that a variable should lie in the range of a neural network... (read more)

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